Causal Relationship Detection for Dynamic Computing Environment Administration
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Solution Overview
Problem
In large, dynamic computing environments, conventional management systems struggle to maintain and adapt relationship models to reflect the complexity of interdependencies, leading to difficulties in managing and troubleshooting due to the exponential growth of infrastructures and the lack of machine intelligence in managing dynamic relationships.
Innovation Solution
A mechanism is introduced to automatically detect causal relationships among system entities using a causal relationship detector and rules converter, which generates graphs based on events to identify causal connectivity and converts these relationships into behavioral rules, enabling proactive management without relying on existing relationship models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional management systems use static relationship models to monitor computing environments, then basic management functionalities are provided, but the systems become expensive and difficult to maintain as infrastructures grow exponentially
Solution Approach 1:
The patent transforms static relationship models into dynamic behavioral rules that automatically adapt to changing computing environments. The system continuously monitors events and updates causal relationships in real-time, allowing the management system to evolve with infrastructure growth without manual intervention, thereby reducing maintenance complexity while maintaining reliability
Solution Approach 2:
The system implements self-service through automated causal relationship discovery and behavioral rule generation. The management system autonomously detects relationships among computing entities, generates appropriate behavioral rules, and updates its own knowledge base without requiring administrator intervention, reducing the burden of maintaining relationship models as infrastructures scale
2Extent of automation
If static rules are used to provide machine intelligence in management systems, then basic automation is achieved, but the systems cannot adapt to all possible dependencies that might arise in dynamic environments
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors computing environment events and uses this feedback to dynamically update causal relationships and behavioral rules. This closed-loop approach enables the system to adapt to emerging dependencies automatically, maintaining high automation while gaining versatility to handle unforeseen relationships in dynamic environments
Solution Approach 2:
The system performs preliminary action by proactively discovering causal relationships and generating behavioral rules before problems occur. By continuously analyzing events and establishing causal links in advance, the system prepares adaptive responses to potential issues, enabling both automation and adaptability to dependencies that haven't yet manifested
3Ease of repair
If relationship models are maintained to provide a priori knowledge of resource interconnections, then troubleshooting is simplified, but the models become difficult to maintain and may not reflect full extent of inter dependencies
Solution Approach 1:
The system autonomously discovers and maintains causal relationships by continuously monitoring computing environment events. This self-service approach ensures that troubleshooting knowledge remains current and comprehensive without manual updates, capturing the full extent of interdependencies as they emerge in the dynamic environment while maintaining ease of troubleshooting
Solution Approach 2:
The system proactively establishes causal relationships and generates behavioral rules in advance before troubleshooting is needed. By continuously discovering relationships and preparing behavioral rules beforehand, the system ensures that complete interdependency information is available when troubleshooting occurs, simplifying repair while preventing information loss
Data Source
AI summary
A management system for determining causal relationships among system entities may include a causal relationship detector configured to receive events from a computing environment having a plurality of entities, and detect causal relationships among the plurality of entities, during runtime of the computing environment, based on the events, and a rules converter configured to convert one or more of the causal relationships into at least one behavioral rule. The at least one behavioral rule may indicate a causal relationship between at least two entities of the plurality of entities.